This project developed, implemented, and demonstrated spatially distributed snow modeling and monitoring procedures for estimating snowmelt inputs to the Sacramento and San Joaquin river basins, California. The Distributed Snow Process Model (DSPM) used a temperature index algorithm, SSARR_grid, for estimating liquid water arriving at the ground surface each hourly time step in each model cell
... [Show full abstract] with 2-km spatial resolution. DSPM used interpolated air temperature and precipitation fields, as well as initial snow conditions. Air temperature interpolation incorporated hourly ambient lapse rates based on observed air temperatures in an inverse-distance- squared technique. Precipitation interpolation used an inverse-distance-squared method. We developed a linear function that specified the melt factor used by SSARR_grid in each cell depending on the accumulated temperature index. In this test, DSPM relied on maps of snow water equivalent (SWE) for initiation and validation. We estimated the spatial distribution of SWE by combining estimates of snow cover area (SCA) with interpolations of SWE based on snow sensor and course measurements. SCA retrieval algorithms used measurements from the NOAA Advanced Very High Resolution Radiometer (AVHRR) to estimate snow extent as fractional cover per pixel. SWE interpolations for this project used an algorithm similar to the temperature interpolation. The results showed that 1) SWE maps predicted by DSPM with no updating over periods up to several days have fair agreement with maps produced from AVHRR merged with ground measurements, 2) cloud cover conditions permit the construction of SWE maps from AVHRR and ground measurements with sufficient frequency to improve model results through updating, and 3) preliminary runs of HEC-HMS to route the snowmelt water capture the timing and magnitude of the flow using these spatially distributed inputs.